The eIF4A inhibitor silvestrol sensitizes T-47D ductal breast carcinoma cells to external-beam radiotherapy
Bibliographic record
Abstract
Purpose eIF4A is an RNA helicase that forms part of the machinery of translation initiation. Proteomic analysis demonstrated eIF4A expression to be at least two-fold greater in a radioresistant derivative of T-47D breast cancer cells compared to parental cells. Inhibition of eIF4A has previously been shown to re-sensitize lymphomas to chemotherapeutic agents that cause DNA damage. The objective of this work is to investigate whether inhibition of eIF4A using silvestrol sensitizes breast cancer cells to radiotherapy in tissue culture, using T-47D as a model system. Methods and materials T-47D cells were incubated in medium containing 0 nM to 1 nM silvestrol either for 24 h prior to irradiation at 0 Gy to 10 Gy, delivered by linear accelerator (LINAC) or continually for six days post irradiation. MTT viability and clonogenic assays were used to quantify response. Results Pre-treatment of T-47D cells with 1 nM silvestrol caused a 34% reduction ( p = 0.014) in viability on irradiation at 2 Gy compared to treatment with a DMSO control, as assessed by MTT assay. Maintenance of cells in 1 nM silvestrol for six days following irradiation at 2 Gy caused a 58% reduction ( p = <0.001) in tumor cell viability. Clonogenic assays performed on cells maintained in 1 nM silvestrol following irradiation showed a dose modifying factor (DMF) of 1.4 ( p = <0.001, one-way ANOVA). Conclusions Low concentrations of silvestrol sensitize T-47D breast cancer cells to radiation with minimal effects on unirradiated cells. This highlights the possible usefulness of eIF4A inhibition in potentiating radiation-induced damage at the tumor site without causing systemic toxicity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".